Tracking the State and Behavior of People in Response to COVID-1 19 Through the Fusion of Multiple Longitudinal Data Streams

Mohamed Amine Bouzaghrane; Hassan Obeid; Drake Hayes; Minnie Chen; Meiqing Li; Madeleine Parker; Daniel A. Rodríguez; Daniel G. Chatman; Karen Trapenberg Frick; Raja Sengupta; Joan Walker

arXiv · 2209.11805

Tracking the State and Behavior of People in Response to COVID-1 19 Through the Fusion of Multiple Longitudinal Data Streams

Mohamed Amine Bouzaghrane·Hassan Obeid·Drake Hayes·Minnie Chen·Meiqing Li·Madeleine Parker·Daniel A. Rodríguez·Daniel G. Chatman·Karen Trapenberg Frick·Raja Sengupta·Joan Walker

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Abstract

The changing nature of the COVID-19 pandemic has highlighted the importance of comprehensively considering its impacts and considering changes over time. Most COVID-19 related research addresses narrowly focused research questions and is therefore limited in addressing the complexities created by the interrelated impacts of the pandemic. Such research generally makes use of only one of either 1) actively collected data such as surveys, or 2) passively collected data. While a few studies make use of both actively and passively collected data, only one other study collects it longitudinally. Here we describe a rich panel dataset of active and passive data from U.S. residents collected between August 2020 and July 2021. Active data includes a repeated survey measuring travel behavior, compliance with COVID-19 mandates, physical health, economic well-being, vaccination status, and other factors. Passively collected data consists of all locations visited by study participants, taken from smartphone GPS data. We also closely tracked COVID-19 policies across counties of residence throughout the study period. Such a dataset allows important research questions to be answered; for example, to determine the factors underlying the heterogeneous behavioral responses to COVID-19 restrictions imposed by local governments. Better information about such responses is critical to our ability to understand the societal and economic impacts of this and future pandemics. The development of this data infrastructure can also help researchers explore new frontiers in behavioral science. The article explains how this approach fills gaps in COVID-19 related data collection; describes the study design and data collection procedures; presents key demographic characteristics of study participants; and shows how fusing different data streams helps uncover behavioral insights.

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Mohamed Amine Bouzaghrane, Hassan Obeid, Drake Hayes, Minnie Chen, Meiqing Li, Madeleine Parker, Daniel A. Rodríguez, Daniel G. Chatman, Karen Trapenberg Frick, Raja Sengupta, Joan Walker. 2022-10-01. Tracking the State and Behavior of People in Response to COVID-1 19 Through the Fusion of Multiple Longitudinal Data Streams. https://arxiv.org/abs/2209.11805

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